Automatic detection of adverse events to predict drug label changes using text and data mining techniques
Purpose: The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Methods: Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. Results: 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Conclusions: Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks.
|Keywords||Adverse event, Machine learning, Pharmacoepidemiology, Signal detection, Text mining|
|Persistent URL||dx.doi.org/10.1002/pds.3493, hdl.handle.net/1765/74143|
|Journal||Pharmacoepidemiology and Drug Safety: an international journal|
Gurulingappa, H, Toldo, L, Rajput, A, Kors, J.A, Taweel, A, & Tayrouz, Y. (2013). Automatic detection of adverse events to predict drug label changes using text and data mining techniques. Pharmacoepidemiology and Drug Safety: an international journal, 22(11), 1189–1194. doi:10.1002/pds.3493